Shaowei Xia

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Mining association rules from large databases is an important problem in data mining. There is a need to develop parallel algorithm for this problem because it is a very costly computation process. However, all proposed parallel algorithms for mining association rules follow the conventional level-wise approach. On a shared-memory multi-processors, they(More)
This paper presents two compensation methods for multilayer perceptrons (MLPs) which are very difficult to train by traditional Back Propagation (BP) methods. For MLPs trapped in local minima, compensating methods can correct the wrong outputs one by one using constructing techniques until all outputs are right, so that the MLPs can skip from the local(More)
In the above paper by Mao-Jain (ibid., vol.7 (1996)), the Mahalanobis distance is used instead of Euclidean distance as the distance measure in order to acquire the hyperellipsoidal clustering. We prove that the clustering cost function is a constant under this condition, so hyperellipsoidal clustering cannot be realized. We also explains why the clustering(More)
Abstiact W'e present a new probtilfisdc cl=-fier, ded SOM-PNN class-tier, for volume data class-fi~on md ~om The new classifier produces probabilistic cl=-fication with Bayesian cofidenu measure which is hi@y desirable in volume rendering Based on the SOM map traind with a large training data set our SOM-P~ classifier performs the probabiic classification(More)
Mining association rules from large databases is very costly. We propose to develop parallel algorithms for this task on shared-memory multiprocessor (SMP). All proposed parallel algorithms for other paradigms follow the conventional level-wise approach : they need as many iterations as the length of the maximum large itemset. To make matter worse, they(More)
In this paper, we produce a new medical image classification scheme using self-organizing map (SOM) combining with multiscale technique. It addresses the problem of the handling of edge pixels in the traditional multiscale SOM classifiers. First, to solve the difficulty with manually selection of edge pixels, a multiscale edge detection algorithm based on(More)